{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"dataset/credit-a.csv\",header=None) #header=None表示数据文件没有表头"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>560.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>280</td>\n",
       "      <td>824.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0      1      2   3   4   5   6     7   8   9   10  11  12   13     14  15\n",
       "0   0  30.83  0.000   0   0   9   0  1.25   0   0   1   1   0  202    0.0  -1\n",
       "1   1  58.67  4.460   0   0   8   1  3.04   0   0   6   1   0   43  560.0  -1\n",
       "2   1  24.50  0.500   0   0   8   1  1.50   0   1   0   1   0  280  824.0  -1\n",
       "3   0  27.83  1.540   0   0   9   0  3.75   0   0   5   0   0  100    3.0  -1\n",
       "4   0  20.17  5.625   0   0   9   0  1.71   0   1   0   1   2  120    0.0  -1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    357\n",
       "-1    296\n",
       "Name: 15, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,-1].value_counts() # 统计最后一列的每个数值及对应的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,:-1]\n",
    "y = data.iloc[:,-1].replace(-1,0) # 将最后一列的-1替换成0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(4,input_shape=(15,),activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(4,activation='relu')) # 第二个隐藏层会自动识别输入\n",
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid')) # 输出层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 4)                 64        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 89\n",
      "Trainable params: 89\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary() # 查看网络结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['acc']) #metrics 输出正确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 1.4963 - acc: 0.6585\n",
      "Epoch 2/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 1.0486 - acc: 0.6708\n",
      "Epoch 3/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.7959 - acc: 0.6585\n",
      "Epoch 4/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.7004 - acc: 0.6233\n",
      "Epoch 5/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6930 - acc: 0.5896\n",
      "Epoch 6/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6901 - acc: 0.5896\n",
      "Epoch 7/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6886 - acc: 0.6049\n",
      "Epoch 8/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6845 - acc: 0.6156\n",
      "Epoch 9/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6789 - acc: 0.6233\n",
      "Epoch 10/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6735 - acc: 0.6202\n",
      "Epoch 11/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6739 - acc: 0.6064\n",
      "Epoch 12/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6744 - acc: 0.6034\n",
      "Epoch 13/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6723 - acc: 0.6156\n",
      "Epoch 14/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6681 - acc: 0.6279\n",
      "Epoch 15/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6681 - acc: 0.6233\n",
      "Epoch 16/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6673 - acc: 0.6294\n",
      "Epoch 17/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6646 - acc: 0.6263\n",
      "Epoch 18/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6657 - acc: 0.6202\n",
      "Epoch 19/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6628 - acc: 0.6263\n",
      "Epoch 20/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6615 - acc: 0.6202\n",
      "Epoch 21/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6619 - acc: 0.6401\n",
      "Epoch 22/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6606 - acc: 0.6386\n",
      "Epoch 23/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6610 - acc: 0.6417\n",
      "Epoch 24/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6579 - acc: 0.6279\n",
      "Epoch 25/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6556 - acc: 0.6325\n",
      "Epoch 26/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6566 - acc: 0.6248\n",
      "Epoch 27/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6544 - acc: 0.6340\n",
      "Epoch 28/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6512 - acc: 0.6478\n",
      "Epoch 29/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6520 - acc: 0.6524\n",
      "Epoch 30/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6488 - acc: 0.6386\n",
      "Epoch 31/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6480 - acc: 0.6554\n",
      "Epoch 32/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6475 - acc: 0.6554\n",
      "Epoch 33/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6458 - acc: 0.6554\n",
      "Epoch 34/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6465 - acc: 0.6585\n",
      "Epoch 35/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6459 - acc: 0.6432\n",
      "Epoch 36/100\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.6450 - acc: 0.6616\n",
      "Epoch 37/100\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.6436 - acc: 0.6539\n",
      "Epoch 38/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6392 - acc: 0.6631\n",
      "Epoch 39/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6377 - acc: 0.6478\n",
      "Epoch 40/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6378 - acc: 0.6646\n",
      "Epoch 41/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6364 - acc: 0.6646\n",
      "Epoch 42/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6334 - acc: 0.6631\n",
      "Epoch 43/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6377 - acc: 0.6708\n",
      "Epoch 44/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6367 - acc: 0.6570\n",
      "Epoch 45/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6305 - acc: 0.6723\n",
      "Epoch 46/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6306 - acc: 0.6692\n",
      "Epoch 47/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6277 - acc: 0.6662\n",
      "Epoch 48/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6264 - acc: 0.6708\n",
      "Epoch 49/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6250 - acc: 0.6769\n",
      "Epoch 50/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6204 - acc: 0.6723\n",
      "Epoch 51/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6185 - acc: 0.6769\n",
      "Epoch 52/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6194 - acc: 0.6845\n",
      "Epoch 53/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6175 - acc: 0.6845\n",
      "Epoch 54/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6215 - acc: 0.6447\n",
      "Epoch 55/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6105 - acc: 0.6876\n",
      "Epoch 56/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.6102 - acc: 0.6738\n",
      "Epoch 57/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5938 - acc: 0.7090\n",
      "Epoch 58/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5909 - acc: 0.7090\n",
      "Epoch 59/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.5838 - acc: 0.7243\n",
      "Epoch 60/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.5867 - acc: 0.7228\n",
      "Epoch 61/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5728 - acc: 0.7259\n",
      "Epoch 62/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.5710 - acc: 0.7397\n",
      "Epoch 63/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5621 - acc: 0.7351\n",
      "Epoch 64/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5571 - acc: 0.7412\n",
      "Epoch 65/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5523 - acc: 0.7289\n",
      "Epoch 66/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5557 - acc: 0.7427\n",
      "Epoch 67/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5431 - acc: 0.7489\n",
      "Epoch 68/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5425 - acc: 0.7443\n",
      "Epoch 69/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5374 - acc: 0.7580\n",
      "Epoch 70/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5374 - acc: 0.7473\n",
      "Epoch 71/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5313 - acc: 0.7580\n",
      "Epoch 72/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5299 - acc: 0.7642\n",
      "Epoch 73/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5207 - acc: 0.7734\n",
      "Epoch 74/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5178 - acc: 0.7734\n",
      "Epoch 75/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5127 - acc: 0.7688\n",
      "Epoch 76/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5195 - acc: 0.7642\n",
      "Epoch 77/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5123 - acc: 0.7779\n",
      "Epoch 78/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5083 - acc: 0.7825\n",
      "Epoch 79/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5361 - acc: 0.7734\n",
      "Epoch 80/100\n",
      "21/21 [==============================] - ETA: 0s - loss: 0.4555 - acc: 0.843 - 0s 1ms/step - loss: 0.5121 - acc: 0.7764\n",
      "Epoch 81/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5055 - acc: 0.7779\n",
      "Epoch 82/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5036 - acc: 0.7856\n",
      "Epoch 83/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4996 - acc: 0.7856\n",
      "Epoch 84/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.4986 - acc: 0.7902\n",
      "Epoch 85/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4923 - acc: 0.7856\n",
      "Epoch 86/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4899 - acc: 0.7963\n",
      "Epoch 87/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4879 - acc: 0.7994\n",
      "Epoch 88/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4812 - acc: 0.7994\n",
      "Epoch 89/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.4766 - acc: 0.8025\n",
      "Epoch 90/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4719 - acc: 0.8116\n",
      "Epoch 91/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.4707 - acc: 0.8116\n",
      "Epoch 92/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4639 - acc: 0.8208\n",
      "Epoch 93/100\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.4567 - acc: 0.8239\n",
      "Epoch 94/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4675 - acc: 0.8147\n",
      "Epoch 95/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4570 - acc: 0.8178\n",
      "Epoch 96/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4476 - acc: 0.8331\n",
      "Epoch 97/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4450 - acc: 0.8254\n",
      "Epoch 98/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4433 - acc: 0.8361\n",
      "Epoch 99/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4459 - acc: 0.8239\n",
      "Epoch 100/100\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4417 - acc: 0.8285\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x,y,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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   "source": [
    "pred = model.predict(x)"
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  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "       [0.628261  ],\n",
       "       [0.3332438 ]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'acc'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys() # history是一个dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23d2588cb88>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch,history.history.get('loss')) #绘制训练次数与损失的变化图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23d25d36448>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.epoch,history.history.get('acc')) #绘制训练次数与正确率的变化图"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
